A previous literature has documented that bond returns are predicted by macroeconomic information not contained in yields contemporaneously. That literature has mostly relied on final revised, rather than real time macroeconomic data. We show that the use of real time data substantially reduces the predictive power of macro variables for future bond returns as well as the implied countercyclicality of term premiums. We discuss potential interpretations of our results.

Equity returns follow a pronounced V-shape pattern around the onset of recessions. They sharply drop into negative territory just before business cycle peaks and then strongly recover as the recession unfolds. Recessions are typically preceded by a flat yield curve. Probit models relying on the term spread as a predictor therefore time the beginning of recessions well. We show that model-implied recession probabilities based on the term spread strongly improve equity premium prediction in- and out-of-sample and outperform several benchmark predictors. Correcting for a structural break in the mean of the term spread in 1982 further strengthens the forecast performance.

We identify a yield news shock as an innovation that does not move Treasury yields contemporaneously but explains a maximum share of their future variation. Yields do not immediately respond to the news shock as the initial reaction of term premiums and expected short rates offset each other. While the impact on term premiums fades quickly, expected short rates and thus yields decline persistently. As a result, the shock explains a staggering 50% of Treasury yield variation several years out. A positive yield news shock is associated with a coincident sharp increase in stock and bond market volatility, a contemporaneous response of leading economic indicators, and is followed by a persistent decline of real activity and inflation which is accommodated by the Federal Reserve. Identified shocks to realized stock market volatility and business cycle news imply similar impulse responses and together capture the bulk of variation of the yield news shock.

A previous literature has documented that bond returns are predicted by macroeconomic information not contained in yields contemporaneously. That literature has mostly relied on final revised, rather than real time macroeconomic data. We show that the use of real time data substantially reduces the predictive power of macro variables for future bond returns as well as the implied countercyclicality of term premiums. We discuss potential interpretations of our results. Received January 26, 2014; editorial decision June 16, 2017 by Editor Geert Bekaert.

We document large average excess returns on U.S. equities in anticipation of monetary policy decisions made at scheduled meetings of the Federal Open Market Committee (FOMC) in the past few decades. These pre‐FOMC returns have increased over time and account for sizable fractions of total annual realized stock returns. While other major international equity indices experienced similar pre‐FOMC returns, we find no such effect in U.S. Treasury securities and money market futures. Other major U.S. macroeconomic news announcements also do not give rise to preannouncement excess equity returns. We discuss challenges in explaining these returns with standard asset pricing theory.

We show how to price the time series and cross section of the term structure of interest rates using a three-step linear regression approach. Our method allows computationally fast estimation of term structure models with a large number of pricing factors. We present specification tests favoring a model using five principal components of yields as factors. We demonstrate that this model outperforms the Cochrane and Piazzesi (2008) four-factor specification in out-of-sample exercises but generates similar in-sample term premium dynamics. Our regression approach can also incorporate unspanned factors and allows estimation of term structure models without observing a zero-coupon yield curve.

This paper uses multilevel factor models to characterize within- and between-block variations as well as idiosyncratic noise in large dynamic panels. Block-level shocks are distinguished from genuinely common shocks, and the estimated block-level factors are easy to interpret. The framework achieves dimension reduction and yet explicitly allows for heterogeneity between blocks. The model is estimated using an MCMC algorithm that takes into account the hierarchical structure of the factors. The importance of block-level variations is illustrated in a four-level model estimated on a panel of 445 series related to different categories of real activity in the United States.

We propose regression-based estimators for beta representations of dynamic asset pricing models with an affine pricing kernel specification. We allow for state variables that are cross-sectional pricing factors, forecasting variables for the price of risk, and factors that are both. The estimators explicitly allow for time-varying prices of risk, time-varying betas, and serially dependent pricing factors. Our approach nests the Fama-MacBeth two-pass estimator as a special case. We provide asymptotic multistage standard errors necessary to conduct inference for asset pricing tests. We illustrate our new estimators in an application to the joint pricing of stocks and bonds. The application features strongly time-varying, highly significant prices of risk that are found to be quantitatively more important than time-varying betas in reducing pricing errors.